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<div class="title">extract_clusters.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
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<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2010-2011, Willow Garage, Inc.</span></div>
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<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#ifndef PCL_EXTRACT_CLUSTERS_H_</span></div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#define PCL_EXTRACT_CLUSTERS_H_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160; </div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;pcl/pcl_base.h&gt;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160; </div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;pcl/search/pcl_search.h&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160; </div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="keyword">namespace </span>pcl</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;{</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160; </div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;  extractEuclideanClusters (</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;      <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;cloud, <span class="keyword">const</span> boost::shared_ptr&lt;search::Search&lt;PointT&gt; &gt; &amp;tree, </div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;      <span class="keywordtype">float</span> tolerance, std::vector&lt;PointIndices&gt; &amp;clusters, </div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;      <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> min_pts_per_cluster = 1, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_pts_per_cluster = (std::numeric_limits&lt;int&gt;::max) ());</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160; </div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160; </div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  extractEuclideanClusters (</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;cloud, <span class="keyword">const</span> std::vector&lt;int&gt; &amp;indices, </div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;      <span class="keyword">const</span> boost::shared_ptr&lt;search::Search&lt;PointT&gt; &gt; &amp;tree, <span class="keywordtype">float</span> tolerance, std::vector&lt;PointIndices&gt; &amp;clusters, </div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;      <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> min_pts_per_cluster = 1, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_pts_per_cluster = (std::numeric_limits&lt;int&gt;::max) ());</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160; </div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160; </div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Normal&gt; <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  extractEuclideanClusters (</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;      <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;cloud, <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;Normal&gt;</a> &amp;normals, </div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;      <span class="keywordtype">float</span> tolerance, <span class="keyword">const</span> boost::shared_ptr&lt;KdTree&lt;PointT&gt; &gt; &amp;tree, </div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;      std::vector&lt;PointIndices&gt; &amp;clusters, <span class="keywordtype">double</span> eps_angle, </div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;      <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> min_pts_per_cluster = 1, </div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_pts_per_cluster = (std::numeric_limits&lt;int&gt;::max) ())</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;  {</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="keywordflow">if</span> (tree-&gt;getInputCloud ()-&gt;points.size () != cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    {</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;      PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n&quot;</span>, tree-&gt;getInputCloud ()-&gt;points.size (), cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    }</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keywordflow">if</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size () != normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    {</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;      PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Number of points in the input point cloud (%lu) different than normals (%lu)!\n&quot;</span>, cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    }</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160; </div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="comment">// Create a bool vector of processed point indices, and initialize it to false</span></div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    std::vector&lt;bool&gt; processed (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), <span class="keyword">false</span>);</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160; </div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <span class="comment">// Process all points in the indices vector</span></div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); ++i)</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    {</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;      <span class="keywordflow">if</span> (processed[i])</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160; </div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;      std::vector&lt;unsigned int&gt; seed_queue;</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;      <span class="keywordtype">int</span> sq_idx = 0;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;      seed_queue.push_back (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (i));</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;      processed[i] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160; </div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;      <span class="keywordflow">while</span> (sq_idx &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (seed_queue.size ()))</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;      {</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        <span class="comment">// Search for sq_idx</span></div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        <span class="keywordflow">if</span> (!tree-&gt;radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;        {</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;          sq_idx++;</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;        }</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160; </div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 1; j &lt; nn_indices.size (); ++j)             <span class="comment">// nn_indices[0] should be sq_idx</span></div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;          <span class="keywordflow">if</span> (processed[nn_indices[j]])                         <span class="comment">// Has this point been processed before ?</span></div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;            <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160; </div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;          <span class="comment">//processed[nn_indices[j]] = true;</span></div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;          <span class="comment">// [-1;1]</span></div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;          <span class="keywordtype">double</span> dot_p = normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].normal[0] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[0] +</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;                         normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].normal[1] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[1] +</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;                         normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].normal[2] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[2];</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;          <span class="keywordflow">if</span> ( fabs (acos (dot_p)) &lt; eps_angle )</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;          {</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;            processed[nn_indices[j]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;            seed_queue.push_back (nn_indices[j]);</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;          }</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;        }</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160; </div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;        sq_idx++;</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;      }</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160; </div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;      <span class="comment">// If this queue is satisfactory, add to the clusters</span></div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;      <span class="keywordflow">if</span> (seed_queue.size () &gt;= min_pts_per_cluster &amp;&amp; seed_queue.size () &lt;= max_pts_per_cluster)</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;      {</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;        <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> r;</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;        r.indices.resize (seed_queue.size ());</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; seed_queue.size (); ++j)</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;          r.indices[j] = seed_queue[j];</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; </div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;        <span class="comment">// These two lines should not be needed: (can anyone confirm?) -FF</span></div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        std::sort (r.indices.begin (), r.indices.end ());</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;        r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;        r.header = cloud.<a class="code" href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">header</a>;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;        clusters.push_back (r);   <span class="comment">// We could avoid a copy by working directly in the vector</span></div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      }</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    }</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  }</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160; </div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160; </div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> Normal&gt; </div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="keywordtype">void</span> extractEuclideanClusters (</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;      <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;PointT&gt;</a> &amp;cloud, <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud&lt;Normal&gt;</a> &amp;normals, </div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      <span class="keyword">const</span> std::vector&lt;int&gt; &amp;indices, <span class="keyword">const</span> boost::shared_ptr&lt;KdTree&lt;PointT&gt; &gt; &amp;tree, </div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      <span class="keywordtype">float</span> tolerance, std::vector&lt;PointIndices&gt; &amp;clusters, <span class="keywordtype">double</span> eps_angle, </div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;      <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> min_pts_per_cluster = 1, </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;      <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_pts_per_cluster = (std::numeric_limits&lt;int&gt;::max) ())</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;  {</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    <span class="comment">// \note If the tree was created over &lt;cloud, indices&gt;, we guarantee a 1-1 mapping between what the tree returns</span></div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    <span class="comment">//and indices[i]</span></div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    <span class="keywordflow">if</span> (tree-&gt;getInputCloud ()-&gt;points.size () != cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    {</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;      PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n&quot;</span>, tree-&gt;getInputCloud ()-&gt;points.size (), cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    }</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    <span class="keywordflow">if</span> (tree-&gt;getIndices ()-&gt;size () != indices.size ())</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    {</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;      PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Tree built for a different set of indices (%lu) than the input set (%lu)!\n&quot;</span>, tree-&gt;getIndices ()-&gt;size (), indices.size ());</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    }</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    <span class="keywordflow">if</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size () != normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    {</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;      PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Number of points in the input point cloud (%lu) different than normals (%lu)!\n&quot;</span>, cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;      <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    }</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    <span class="comment">// Create a bool vector of processed point indices, and initialize it to false</span></div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    std::vector&lt;bool&gt; processed (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), <span class="keyword">false</span>);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160; </div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <span class="comment">// Process all points in the indices vector</span></div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; indices.size (); ++i)</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    {</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      <span class="keywordflow">if</span> (processed[indices[i]])</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160; </div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;      std::vector&lt;int&gt; seed_queue;</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;      <span class="keywordtype">int</span> sq_idx = 0;</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;      seed_queue.push_back (indices[i]);</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160; </div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;      processed[indices[i]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160; </div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;      <span class="keywordflow">while</span> (sq_idx &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (seed_queue.size ()))</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;      {</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;        <span class="comment">// Search for sq_idx</span></div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        <span class="keywordflow">if</span> (!tree-&gt;radiusSearch (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]], tolerance, nn_indices, nn_distances))</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        {</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;          sq_idx++;</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        }</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160; </div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 1; j &lt; nn_indices.size (); ++j)             <span class="comment">// nn_indices[0] should be sq_idx</span></div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        {</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;          <span class="keywordflow">if</span> (processed[nn_indices[j]])                             <span class="comment">// Has this point been processed before ?</span></div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;            <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160; </div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;          <span class="comment">//processed[nn_indices[j]] = true;</span></div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;          <span class="comment">// [-1;1]</span></div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;          <span class="keywordtype">double</span> dot_p =</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;            normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices[i]].normal[0] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices[nn_indices[j]]].normal[0] +</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;            normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices[i]].normal[1] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices[nn_indices[j]]].normal[1] +</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;            normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices[i]].normal[2] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices[nn_indices[j]]].normal[2];</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;          <span class="keywordflow">if</span> ( fabs (acos (dot_p)) &lt; eps_angle )</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;          {</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;            processed[nn_indices[j]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;            seed_queue.push_back (nn_indices[j]);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;          }</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        }</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160; </div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        sq_idx++;</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;      }</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160; </div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;      <span class="comment">// If this queue is satisfactory, add to the clusters</span></div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;      <span class="keywordflow">if</span> (seed_queue.size () &gt;= min_pts_per_cluster &amp;&amp; seed_queue.size () &lt;= max_pts_per_cluster)</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;      {</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;        <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> r;</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        r.indices.resize (seed_queue.size ());</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; seed_queue.size (); ++j)</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;          r.indices[j] = seed_queue[j];</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160; </div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;        <span class="comment">// These two lines should not be needed: (can anyone confirm?) -FF</span></div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;        std::sort (r.indices.begin (), r.indices.end ());</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160; </div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        r.header = cloud.<a class="code" href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">header</a>;</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;        clusters.push_back (r);</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;      }</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    }</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;  }</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160; </div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160; </div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt;</div>
<div class="line"><a name="l00295"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html">  295</a></span>&#160;  <span class="keyword">class </span><a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html">EuclideanClusterExtraction</a>: <span class="keyword">public</span> <a class="code" href="classpcl_1_1_p_c_l_base.html">PCLBase</a>&lt;PointT&gt;</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;  {</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    <span class="keyword">typedef</span> <a class="code" href="classpcl_1_1_p_c_l_base.html">PCLBase&lt;PointT&gt;</a> <a class="code" href="classpcl_1_1_p_c_l_base.html">BasePCLBase</a>;</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160; </div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;      <span class="keyword">typedef</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointT&gt;</a> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud</a>;</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> PointCloud::Ptr PointCloudPtr;</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> PointCloud::ConstPtr PointCloudConstPtr;</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160; </div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code" href="classpcl_1_1search_1_1_search.html">pcl::search::Search&lt;PointT&gt;</a> <a class="code" href="classpcl_1_1search_1_1_search.html">KdTree</a>;</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> pcl::search::Search&lt;PointT&gt;::Ptr KdTreePtr;</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160; </div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;      <span class="keyword">typedef</span> PointIndices::Ptr PointIndicesPtr;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;      <span class="keyword">typedef</span> PointIndices::ConstPtr PointIndicesConstPtr;</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160; </div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160; </div>
<div class="line"><a name="l00312"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a61ba0904c365e4eb40a441e8d1dcf938">  312</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a61ba0904c365e4eb40a441e8d1dcf938">EuclideanClusterExtraction</a> () : <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#af6121587def5c2456a9ad7a0dd429bc2">tree_</a> (), </div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;                                      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a0c681cd8d1d06bebd64070f02e383880">cluster_tolerance_</a> (0),</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;                                      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ae6b3dc18e96b73836ee53107f03d4694">min_pts_per_cluster_</a> (1), </div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;                                      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a2cef6b991e7bb0ef4f96048fbf8fea35">max_pts_per_cluster_</a> (std::numeric_limits&lt;int&gt;::max ())</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;      {};</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160; </div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00322"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#ac4162a11c1fd5797d507068a056bfbf7">  322</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ac4162a11c1fd5797d507068a056bfbf7">setSearchMethod</a> (<span class="keyword">const</span> KdTreePtr &amp;tree) </div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;      { </div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;        <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#af6121587def5c2456a9ad7a0dd429bc2">tree_</a> = tree; </div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;      }</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160; </div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;      <span class="keyword">inline</span> KdTreePtr </div>
<div class="line"><a name="l00331"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#ad91d5929e06b257969c4747a65f98b7d">  331</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ad91d5929e06b257969c4747a65f98b7d">getSearchMethod</a> ()<span class="keyword"> const </span></div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;<span class="keyword">      </span>{ </div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#af6121587def5c2456a9ad7a0dd429bc2">tree_</a>); </div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;      }</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160; </div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00340"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a8fb42fea2e8bfca4ebadf4339335cf11">  340</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a8fb42fea2e8bfca4ebadf4339335cf11">setClusterTolerance</a> (<span class="keywordtype">double</span> tolerance) </div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;      { </div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;        <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a0c681cd8d1d06bebd64070f02e383880">cluster_tolerance_</a> = tolerance; </div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;      }</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160; </div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">double</span> </div>
<div class="line"><a name="l00347"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a7b723a37211039ad47f10b85d72f3509">  347</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a7b723a37211039ad47f10b85d72f3509">getClusterTolerance</a> ()<span class="keyword"> const </span></div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;<span class="keyword">      </span>{ </div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a0c681cd8d1d06bebd64070f02e383880">cluster_tolerance_</a>); </div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;      }</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160; </div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00356"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a096af3508dd19b23a726a8323f7c7bba">  356</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a096af3508dd19b23a726a8323f7c7bba">setMinClusterSize</a> (<span class="keywordtype">int</span> min_cluster_size) </div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;      { </div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;        <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ae6b3dc18e96b73836ee53107f03d4694">min_pts_per_cluster_</a> = min_cluster_size; </div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;      }</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160; </div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">int</span> </div>
<div class="line"><a name="l00363"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#aea9c462ed0ba91f6591f39a04d1eb6e3">  363</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#aea9c462ed0ba91f6591f39a04d1eb6e3">getMinClusterSize</a> ()<span class="keyword"> const </span></div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;<span class="keyword">      </span>{ </div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ae6b3dc18e96b73836ee53107f03d4694">min_pts_per_cluster_</a>); </div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;      }</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160; </div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00372"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#adb0be906f101b309506cdc37ffd31624">  372</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#adb0be906f101b309506cdc37ffd31624">setMaxClusterSize</a> (<span class="keywordtype">int</span> max_cluster_size) </div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;      { </div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;        <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a2cef6b991e7bb0ef4f96048fbf8fea35">max_pts_per_cluster_</a> = max_cluster_size; </div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;      }</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160; </div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">int</span> </div>
<div class="line"><a name="l00379"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a3d03f355ffefec11b31bfa26ee06c7ec">  379</a></span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a3d03f355ffefec11b31bfa26ee06c7ec">getMaxClusterSize</a> ()<span class="keyword"> const </span></div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;<span class="keyword">      </span>{ </div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a2cef6b991e7bb0ef4f96048fbf8fea35">max_pts_per_cluster_</a>); </div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;      }</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160; </div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;      <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a41e0cd5e3f7967d59013c967c909585c">extract</a> (std::vector&lt;PointIndices&gt; &amp;clusters);</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160; </div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;      <span class="comment">// Members derived from the base class</span></div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">BasePCLBase::input_</a>;</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">BasePCLBase::indices_</a>;</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">BasePCLBase::initCompute</a>;</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">BasePCLBase::deinitCompute</a>;</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160; </div>
<div class="line"><a name="l00398"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#af6121587def5c2456a9ad7a0dd429bc2">  398</a></span>&#160;      KdTreePtr <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#af6121587def5c2456a9ad7a0dd429bc2">tree_</a>;</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160; </div>
<div class="line"><a name="l00401"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a0c681cd8d1d06bebd64070f02e383880">  401</a></span>&#160;      <span class="keywordtype">double</span> <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a0c681cd8d1d06bebd64070f02e383880">cluster_tolerance_</a>;</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160; </div>
<div class="line"><a name="l00404"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#ae6b3dc18e96b73836ee53107f03d4694">  404</a></span>&#160;      <span class="keywordtype">int</span> <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ae6b3dc18e96b73836ee53107f03d4694">min_pts_per_cluster_</a>;</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160; </div>
<div class="line"><a name="l00407"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a2cef6b991e7bb0ef4f96048fbf8fea35">  407</a></span>&#160;      <span class="keywordtype">int</span> <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a2cef6b991e7bb0ef4f96048fbf8fea35">max_pts_per_cluster_</a>;</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160; </div>
<div class="line"><a name="l00410"></a><span class="lineno"><a class="line" href="classpcl_1_1_euclidean_cluster_extraction.html#a88e1862f3484b0d07c75a6b37bd22a38">  410</a></span>&#160;      <span class="keyword">virtual</span> std::string <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a88e1862f3484b0d07c75a6b37bd22a38">getClassName</a> ()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> (<span class="stringliteral">&quot;EuclideanClusterExtraction&quot;</span>); }</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160; </div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;  };</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160; </div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;  <span class="keyword">inline</span> <span class="keywordtype">bool</span> </div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;  comparePointClusters (<span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> &amp;a, <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> &amp;b)</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;  {</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    <span class="keywordflow">return</span> (a.indices.size () &lt; b.indices.size ());</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;  }</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;}</div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160; </div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;<span class="preprocessor">#ifdef PCL_NO_PRECOMPILE</span></div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;<span class="preprocessor">#include &lt;pcl/segmentation/impl/extract_clusters.hpp&gt;</span></div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160; </div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">//#ifndef PCL_EXTRACT_CLUSTERS_H_</span></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html">pcl::EuclideanClusterExtraction</a></div><div class="ttdoc">EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sen...</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:296</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a096af3508dd19b23a726a8323f7c7bba"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a096af3508dd19b23a726a8323f7c7bba">pcl::EuclideanClusterExtraction::setMinClusterSize</a></div><div class="ttdeci">void setMinClusterSize(int min_cluster_size)</div><div class="ttdoc">Set the minimum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:356</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a0c681cd8d1d06bebd64070f02e383880"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a0c681cd8d1d06bebd64070f02e383880">pcl::EuclideanClusterExtraction::cluster_tolerance_</a></div><div class="ttdeci">double cluster_tolerance_</div><div class="ttdoc">The spatial cluster tolerance as a measure in the L2 Euclidean space.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:401</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a2cef6b991e7bb0ef4f96048fbf8fea35"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a2cef6b991e7bb0ef4f96048fbf8fea35">pcl::EuclideanClusterExtraction::max_pts_per_cluster_</a></div><div class="ttdeci">int max_pts_per_cluster_</div><div class="ttdoc">The maximum number of points that a cluster needs to contain in order to be considered valid (default...</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:407</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a3d03f355ffefec11b31bfa26ee06c7ec"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a3d03f355ffefec11b31bfa26ee06c7ec">pcl::EuclideanClusterExtraction::getMaxClusterSize</a></div><div class="ttdeci">int getMaxClusterSize() const</div><div class="ttdoc">Get the maximum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:379</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a41e0cd5e3f7967d59013c967c909585c"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a41e0cd5e3f7967d59013c967c909585c">pcl::EuclideanClusterExtraction::extract</a></div><div class="ttdeci">void extract(std::vector&lt; PointIndices &gt; &amp;clusters)</div><div class="ttdoc">Cluster extraction in a PointCloud given by &lt;setInputCloud (), setIndices ()&gt;</div><div class="ttdef"><b>Definition:</b> extract_clusters.hpp:210</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a61ba0904c365e4eb40a441e8d1dcf938"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a61ba0904c365e4eb40a441e8d1dcf938">pcl::EuclideanClusterExtraction::EuclideanClusterExtraction</a></div><div class="ttdeci">EuclideanClusterExtraction()</div><div class="ttdoc">Empty constructor.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:312</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a7b723a37211039ad47f10b85d72f3509"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a7b723a37211039ad47f10b85d72f3509">pcl::EuclideanClusterExtraction::getClusterTolerance</a></div><div class="ttdeci">double getClusterTolerance() const</div><div class="ttdoc">Get the spatial cluster tolerance as a measure in the L2 Euclidean space.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:347</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a88e1862f3484b0d07c75a6b37bd22a38"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a88e1862f3484b0d07c75a6b37bd22a38">pcl::EuclideanClusterExtraction::getClassName</a></div><div class="ttdeci">virtual std::string getClassName() const</div><div class="ttdoc">Class getName method.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:410</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a8fb42fea2e8bfca4ebadf4339335cf11"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a8fb42fea2e8bfca4ebadf4339335cf11">pcl::EuclideanClusterExtraction::setClusterTolerance</a></div><div class="ttdeci">void setClusterTolerance(double tolerance)</div><div class="ttdoc">Set the spatial cluster tolerance as a measure in the L2 Euclidean space</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:340</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_ac4162a11c1fd5797d507068a056bfbf7"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#ac4162a11c1fd5797d507068a056bfbf7">pcl::EuclideanClusterExtraction::setSearchMethod</a></div><div class="ttdeci">void setSearchMethod(const KdTreePtr &amp;tree)</div><div class="ttdoc">Provide a pointer to the search object.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:322</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_ad91d5929e06b257969c4747a65f98b7d"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#ad91d5929e06b257969c4747a65f98b7d">pcl::EuclideanClusterExtraction::getSearchMethod</a></div><div class="ttdeci">KdTreePtr getSearchMethod() const</div><div class="ttdoc">Get a pointer to the search method used.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:331</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_adb0be906f101b309506cdc37ffd31624"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#adb0be906f101b309506cdc37ffd31624">pcl::EuclideanClusterExtraction::setMaxClusterSize</a></div><div class="ttdeci">void setMaxClusterSize(int max_cluster_size)</div><div class="ttdoc">Set the maximum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:372</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_ae6b3dc18e96b73836ee53107f03d4694"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#ae6b3dc18e96b73836ee53107f03d4694">pcl::EuclideanClusterExtraction::min_pts_per_cluster_</a></div><div class="ttdeci">int min_pts_per_cluster_</div><div class="ttdoc">The minimum number of points that a cluster needs to contain in order to be considered valid (default...</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:404</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_aea9c462ed0ba91f6591f39a04d1eb6e3"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#aea9c462ed0ba91f6591f39a04d1eb6e3">pcl::EuclideanClusterExtraction::getMinClusterSize</a></div><div class="ttdeci">int getMinClusterSize() const</div><div class="ttdoc">Get the minimum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:363</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_af6121587def5c2456a9ad7a0dd429bc2"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#af6121587def5c2456a9ad7a0dd429bc2">pcl::EuclideanClusterExtraction::tree_</a></div><div class="ttdeci">KdTreePtr tree_</div><div class="ttdoc">A pointer to the spatial search object.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:398</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase</a></div><div class="ttdoc">PCL base class. Implements methods that are used by most PCL algorithms.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:69</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a09c70d8e06e3fb4f07903fe6f8d67869"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">pcl::PCLBase::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">The input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:150</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_aaee847c8a517ebf365bad2cb182a6626"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">pcl::PCLBase::indices_</a></div><div class="ttdeci">IndicesPtr indices_</div><div class="ttdoc">A pointer to the vector of point indices to use.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:153</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_acceb20854934f4cf77e266eb5a44d4f0"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">pcl::PCLBase::initCompute</a></div><div class="ttdeci">bool initCompute()</div><div class="ttdoc">This method should get called before starting the actual computation.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:139</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_afc426c4eebb94b7734d4fa556bff1420"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">pcl::PCLBase::deinitCompute</a></div><div class="ttdeci">bool deinitCompute()</div><div class="ttdoc">This method should get called after finishing the actual computation.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:174</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a82e0be055a617e5e74102ed62712b352"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">pcl::PointCloud::header</a></div><div class="ttdeci">pcl::PCLHeader header</div><div class="ttdoc">The point cloud header. It contains information about the acquisition time.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:407</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_search_html"><div class="ttname"><a href="classpcl_1_1search_1_1_search.html">pcl::search::Search</a></div><div class="ttdoc">Generic search class. All search wrappers must inherit from this.</div><div class="ttdef"><b>Definition:</b> search.h:75</div></div>
<div class="ttc" id="astructpcl_1_1_point_indices_html"><div class="ttname"><a href="structpcl_1_1_point_indices.html">pcl::PointIndices</a></div><div class="ttdef"><b>Definition:</b> PointIndices.h:13</div></div>
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